scholarly journals Optimization of charge energy prediction by BP neural networks based on a genetic algorithm

2021 ◽  
Vol 10 (3) ◽  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Bp Neural Networks ◽  
Energy Prediction ◽  
Charge Energy

Improved Artificial BP Neural Networks and Genetic Algorithm Used to Predict the Purity of the Artificial Synthetic Hydrotalcite

2007 ◽  
Vol 280-283 ◽  
pp. 495-498
Author(s):  
Qiang Luo ◽  
Qing Li Ren
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Prediction Model ◽  
Process Parameters ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Raw Material ◽  
Algorithm Analysis ◽  
Neural Net ◽  
Bp Neural Networks

A prediction model for purity of the artificial synthetic hydrotalcite under varied process parameters based on improved artificial back-propagation (BP) neural networks is developed. And the non-linear relationship between the hydrotalcite purity and the raw material adding amount of NaOH, MgCl2 and AlCl3 was established based on BP learning algorithm analysis and convergence improvement. The hydrotalcite purity can be predicted by means of the trained neural net. Thus, by virtue of the prediction model, the future hydrotalcite purity can be evaluated under random complicated raw material amounts. Moreover, the best processing technology is optimized using the genetic algorithm.

Vehicle Type Recognition Using in Geomagnetic Data Based on Neural Network

2013 ◽  
Vol 313-314 ◽  
pp. 1380-1384
Author(s):  
Rui Qing Kang ◽  
Xi Sheng Li ◽  
Hai Jian Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Recognition Rate ◽  
Intelligent Transportation ◽  
Geomagnetic Disturbance ◽  
Bp Neural Networks ◽  
Vehicle Type ◽  
Geomagnetic Data

Vehicle Type Recognition is the base and key point for Intelligent Transportation,Through the geomagnetic disturbance data of different vehicle type, constituting a sort of BP neural networks, and optimizing it using Genetic Algorithm. The result is good. This method can raise the recognition rate effectively and reduce the quantity of calculating. It has strong practicability.

Optimizing BP Networks by Means of Genetic Algorithms in Quality of Laser Milling

2013 ◽  
Vol 690-693 ◽  
pp. 3338-3342
Author(s):  
Zhao Mei Xu ◽  
Zong Hai Hong ◽  
Gang Yang ◽  
Qing An Wang
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Back Propagation ◽  
Maximum Relative Error ◽  
Bp Neural Networks ◽  
Technical Parameters ◽  
Evolutionary Neural Network ◽  
Laser Milling ◽  
Real Value ◽  
Accuracy Of Prediction

Artificial neural networks were introduced in the area of laser milling. The prediction model of surface quality in laser milling parts, including the width, depth of cladding layer, was proposed based on the back propagation (BP) neural networks. The model combined the global optimization searching performance of the genetic algorithm and the localization of the back propagation (BP) neural networks. Five technical parameters were selected to test the reliability of the model. The simulation and experimental results show that the evolutionary neural network based on genetic algorithm can effectively overcome the problem of falling into local minimum point. This method can get higher accuracy of prediction. It improves the measurement precision with the maximum relative error 2. 21% between the predicted content and the real value.

Optimizing Design and Application of BP Neural Networks Based on Genetic Algorithm

2011 ◽  
Vol 317-319 ◽  
pp. 245-249
Author(s):  
Wang Jie Niu ◽  
Er Guang Qu ◽  
Chun Yan Liu
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Learning Rate ◽  
Learning Ability ◽  
Bp Neural Networks ◽  
Inertia Coefficient ◽  
Optimizing Design ◽  
Self Learning ◽  
Design And Application

Genetic algorithm optimize weight’s volume of Neural Networks by optimizing learning rate and inertia coefficient, which overcome the BP algorithmic shortcoming of easy into the part extreme, and have ensured BP algorithmic training accuracy, and makes it have higher self-adaptability and self-learning ability.

Application Research of Adaptive Control Techniques in the Hydraulic Torque Converter's Buffer Locking Process

2012 ◽  
Vol 588-589 ◽  
pp. 1495-1498
Author(s):  
Yi Jin ◽  
Wei Ping Liu ◽  
Xi Xia Liu
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Adaptive Control ◽  
Back Propagation ◽  
Torque Converter ◽  
Bp Neural Networks ◽  
Control Techniques ◽  
Control Precision ◽  
The Neural Network ◽  
Hydraulic Torque Converter

When hydraulic torque converter is applied in hydraulic transmission-vehicle, control precision in buffer locking process of hydraulic torque converter was easily disturbed by friction plate's abrasion, changed buffer slope and other factors, which accordingly caused Impact to transmission system of vehicle. In this paper, adaptive control techniques was applied in buffer locking process as a solution to improve control precision, on basis of Back Propagation (BP) Neural Networks and Genetic Algorithm (GA). In the research, the BP Neural Networks and PID control algorithm was designed to control buffer locking process and Genetic Algorithm was applied to optimize the neural network parameters. Based on AMEsim and Matlab/simulink, joint simulation was carried out. The simulation result shows that Adaptive Control Techniques based on GA-BP can control the locking process fast and accurately.

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